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Generic accelerated sequence alignment in SeqAn using vectorization and multi-threading.

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Summary
This summary is machine-generated.

We developed a new bioinformatics module that accelerates pairwise sequence alignment using single instruction multiple data (SIMD) and thread-level parallelization. This tool significantly speeds up alignment computations on modern processors.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Pairwise sequence alignment is a fundamental technique in bioinformatics.
  • Existing methods can be computationally intensive, limiting large-scale analyses.

Purpose of the Study:

  • To present a generically accelerated module for pairwise sequence alignments.
  • To enhance the efficiency of sequence alignment for diverse bioinformatics applications.

Main Methods:

  • Unified dynamic programming kernel with generalized inter-sequence vectorization.
  • Exploited single instruction multiple data (SIMD) instructions for simultaneous alignments.
  • Implemented two layers of thread-level parallelization: distributing alignments and parallelizing single computations.

Main Results:

  • Evaluated on Intel® Xeon® (Skylake) and Intel® Xeon PhiTM (KNL) processors.
  • Achieved significant speedups: 1600x faster on Xeon PhiTM and 1400x faster on Xeon® compared to sequential methods.
  • Demonstrated performance improvements with AVX512-BW instruction set.

Conclusions:

  • The developed module offers substantial performance gains for pairwise sequence alignment.
  • The generic acceleration is applicable to a broad range of bioinformatics tasks.
  • The module is publicly available and supports various SIMD instruction sets.